Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data

2006 ◽  
Vol 51 (3) ◽  
pp. 1643-1655 ◽  
Author(s):  
Yongdai Kim ◽  
Sunghoon Kwon ◽  
Seuck Heun Song
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Multiple Cancer ◽  
Sparse Logistic Regression ◽  
Cancer Types

scholarly journals Robust Sparse Logistic Regression With the $L_{q}$ ($0 < \text{q} < 1$ ) Regularization for Feature Selection Using Gene Expression Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68586-68595 ◽  
Author(s):  
Ziyi Yang ◽  
Yong Liang ◽  
Hui Zhang ◽  
Hua Chai ◽  
Bowen Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Expression Data ◽  
Sparse Logistic Regression

scholarly journals A Comparative Analysis of Gene-Expression Data of Multiple Cancer Types

PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13696 ◽  
Author(s):  
Kun Xu ◽  
Juan Cui ◽  
Victor Olman ◽  
Qing Yang ◽  
David Puett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Multiple Cancer ◽  
Cancer Types

scholarly journals Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

2021 ◽  
Vol 9 ◽  
Author(s):  
Guangyao Shan ◽  
Huan Zhang ◽  
Guoshu Bi ◽  
Yunyi Bian ◽  
Jiaqi Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gene Expression Data ◽  
Cancer Cell Lines ◽  
Expression Data ◽  
Rna Seq ◽  
Multiple Cancer ◽  
Related Model ◽  
Cancer Types

Background: Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.Methods and Materials: We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.Results: After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p &lt; 0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC = 6.58, p &lt; 0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC = 0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.Conclusion: This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.

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